Purpose: We sought to construct and validate a model that predicts a medical student’s chances of matching into an obstetrics and gynecology residency.
Background: Obstetrics and gynecology clerkship directors and medical students alike desire reliable prognostic matching information tailored to the individual. One predictive tool is the nomogram, which creates a simple graphical representation of a statistical predictive model that generates a numerical probability of an event.
Methods: Seven residencies from across the country received local institutional review board approval and then shared their 2017 to 2020 applicant data. Multiple logistic models were fit onto the training cohort to predict matching. Variables were removed using least absolute shrinkage (LASSO) and selection operator reduction to find the best parsimonious model. Calibration curves were plotted to inform educators about the accuracy of predicted probabilities.
Results: In total, 7,136 eligible medical students applied to the seven OBGYN residencies were included. The rate of those applicants matching into a US OBGYN residency was 54.4% (95% confidence interval 53.2%-55.6%). The model had excellent discrimination and calibration during internal validation (bias-corrected concordance index) and maintained accuracy during temporal validation using the separate validation cohort (concordance index). The Brier score for the model was 0.2 with an area under the curve of 0.76. Clerkship directors in OBGYN performed slightly more favorably than the model when predicting who would match.
Discussions: This model predicts a medical student\'s chances of matching into an obstetrics and gynecology residency may facilitate improved counseling and fewer unmatched medical students.
Topics: CREOG & APGO Annual Meeting, 2022, Student, Faculty, Clerkship Director, Residency Director, Practice-Based Learning & Improvement, UME, Assessment,
Tyler Muffly, MD, Denver Health; Meredith Alston, MD; Jill Liss, MD; Nicki Nguyen, MD; Janet Corrall, PhD; Eric Jelovsek, MD
